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Abstract

Clinical analysis of light scattering from cellular organelle distributions can help identify disease and predict a patient's response to treatment. This work presents a theoretical basis for the identification of important intracellular distributions from scattering patterns even in the presence of optical and structural variability, and examines how the geometry of an organelle distribution affects key properties of wide-angle (two-dimensional) scattering patterns. Specifically, this work demonstrates how organelle arrangement relates to the size and shape of intensity peaks within simulated scattering images, and how this relationship can affect cell identification when using standard image classification methods.

Figures (5)

Schematic diagram of the geometry used in scattering amplitude calculation. The organelle scattering distribution is located 5mm below a 3mm x 3mm receptive field, which runs parallel to the direction of the incident light.

Example scattering simulations for perinuclear (A), diffuse (B), and peripheral (C) distributions, each made up of 250 organelles and simulated using non-shifted amplitude calculation, Eq. (1). As the size and shape of the distribution changes, so does the size and shape of the intensity regions within its scattering image [4,12].

LEFT: The effect of a phase perturbation on a population's simulated scattering pattern, using a very wide distribution aperture (minimal shape effects): scattering using shifted phase values (A), and scattering using non-shifted phase values (B) for 1000 scatterers, placed randomly in a 1500µm radius sphere. RIGHT: Comparison of the simulated wide-angle scattering patterns for a mitochondrial distribution simulated by the mtPatterns algorithm, using the shifted equation (C) and non-shifted equation (D) for 300 scatterers, placed randomly in a diffuse cellular distribution with an inner and outer radius of 4µm and 8µm respectively. In both cases, average peak breadth does not change noticeably due to phase perturbation.

Comparison of simulated scattering pattern data sets in terms of standard image descriptors representing the edge (A) and spot (B) content in scattering images. Edge content is represented by the Laws’ Texture Energy E5 kernel (E5) while spot content is represented by the Laws’ Texture Energy S5 kernel, scaled by a factor of two (S5x2). Tests were performed for three different distribution types, with fifteen samples per distribution, per trial. As shown here, the difference between texture values for different organelle distribution classes was much greater than the difference observed between the non-shifted and shifted patterns.

Comparison of shifted/non-shifted image differences in terms of twenty-one texture features, specifically: (A) the ratio of the within-group (i.e., distribution class) standard deviation to the average separation between groups, and (B) the ratio of the difference between shifted/non-shifted cases to the average between-group separation and to the average within-group standard deviation.